Gümüşhane ÜniversitesiGümüşhane ÜniversitesiAçık Erişim Arşivi · RDL Open

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  • MakaleYalnız künye2026
    A novel neutrosophic estimation method for handling uncertainty in population mean estimation

    Yadav, Akhilesh; Yadav, Dharmendra Kumar; Yadav, Subhash Kumar; Zaman, Tolga

    Estimating the population mean using auxiliary information has been extensively explored within the classical framework. While point estimators offer simplicity, they provide only a single value without reflecting the uncertainty or variability inherent in real-world data. This shortcoming is particularly critical in high-precision applications and decision-making contexts. Moreover, classical estimators are often vulnerable to the influence of outliers, which can distort results and introduce significant bias. To address these limitations, this study introduces a novel estimator grounded in neutrosophic theory, which is specifically designed to handle imprecise, vague, and incomplete information-conditions frequently encountered in practical sampling scenarios. The proposed neutrosophic estimator incorporates auxiliary information expressed in neutrosophic terms, offering a more flexible and resilient estimation strategy. We derive expressions for the bias and mean square error of the proposed estimator under a first-order approximation. A comprehensive theoretical analysis, supported by simulation studies, confirms that the neutrosophic estimator consistently outperforms classical estimators in terms of accuracy and robustness, especially in uncertain environments. These findings underscore the potential of neutrosophic approaches as powerful alternatives to conventional estimation techniques in modern statistical inference.

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  • MakaleYalnız künye2026
    Improved generalized ratio estimators of population mean using auxiliary variables

    Yadav, Subhash Kumar; Arya, Diksha; Sharma, Prayas; Zaman, Tolga

    The Quest for the improved estimators of population mean is a continuous process to estimate it more efficiently and more closely to the true population mean of the study variable. There are various estimators of population mean in the literature, which estimate it efficiently, but still there is space for more efficient estimators. For improved estimation of population mean of primary variable, we suggest six different searls type ratio estimators utilizing the known auxiliary parameters. We derive the expressions for the biases and the mean squared errors of the introduced estimators for an approximation of degree one. The optimal values of the Searls characterizing constants, which minimize the mean squared errors of the introduced estimators, are obtained. The least value of the mean squared error of the proposed estimators for these optimal values of the characterizing scalars is also acquired. The efficiency of the proposed estimators is compared theoretically with the competing estimators of population mean through their mean squared errors. We derive the efficiency conditions of the proposed estimator for which it is more efficient than the estimators of population mean in competition. These efficiency conditions of the proposed estimators are verified through empirical data and the improvement over the competing estimators is calculated based on the minimum values of the mean squared errors. It is evident from the numerical study that the proposed estimator has the least mean squared error and the highest percentage relative efficiency in comparison to competing estimators. Thus, the proposed estimators are recommended for applications in different areas of applications.

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  • MakaleYalnız künye2025
    Estimation of Population Mean Using Some Improved Imputation Methods for Missing Data in Sample Surveys

    Pandey, M.K.; Singh, G.N.; Zaman, Togla

    Abstract.: In this research article, we present novel imputation methods designed to address missing data challenges in sample surveys. We then introduce innovative estimation procedures for calculating population means based on these methods. Our study thoroughly examines the properties of these new estimation procedures, assessing their biases and mean square errors. Through the use of both real and simulated data sets, we demonstrate the superior performance of our proposed estimators compared to existing methods in similar scenarios. In conclusion, we offer practical recommendations for survey practitioners.

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  • MakaleAçık Erişim2025
    Log-transformed approaches to variance estimation using auxiliary data

    Singh, Poonam; Sharma, Prayas; Singh, Anjali; Zaman, Tolga; Emam, Walid

    Reliable statistical inference requires accurate population variance estimate, especially in domains where measurement limitations and intrinsic variability impact data. Applying traditional estimators to skewed or heavy-tailed populations frequently results in inefficient results. We provide a novel class of generalized logarithmic variance estimators that use logarithmic transformations and auxiliary data to stabilize variance and improve estimator performance in order to overcome this constraint. We calculate the suggested estimators' bias and Mean Squared Error (MSE) expressions and assess their effectiveness using comprehensive Monte Carlo simulations with different sample sizes. The suggested estimator, Tr1-, produces a significant reduction in MSE up to 57% increase in efficiency (PRE) at n=600 when compared to the usual estimator. The suggested methodologies resilience and suitability for high-variability situations are further confirmed using real-world datasets. In every context, the findings show that the suggested estimators perform better than the current ones in terms of lower MSE and greater PRE. This research demonstrates how logarithmic transformations may be used to create variance estimators that are more accurate and effective, especially when auxiliary variables are provided.

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  • MakaleAçık Erişim2024
    AN EFFICIENT FAMILY OF RATIO TYPE ESTIMATORS FOR SIMPLE RANDOM SAMPLING

    Yadav, Subhash Kumar; Arya, Diksha; Koc, Tuba; Zaman, Tolga

    The study provides an enhanced estimation of the population mean using known information on an auxiliary variable. An enhanced class of estimators is suggested for the same. The proposed estimator's bias and mean squared error (MSE) are calculated up to the first order of approximation. The optimum values of the characterizing constants are obtained by minimizing the MSE of the proposed estimator. The minimum MSE and the bias values are achieved by optimising the characterizing scalar. The MSE of the proposed estimator has also been compared both conceptually and empirically with the MSEs of competing estimators. Real and simulated data sets are adopted to verify the theoretical prerequisites for the proposed estimator's greater efficiency over competing estimators. The most efficient estimator is recommended for practical utility in different areas of applications and the suggested estimator filfills the requirement.

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  • MakaleYalnız künye2024
    An efficient Hartley–Ross type estimators of nonsensitive and sensitive variables using robust regression methods in sample surveys

    Zaman, Tolga; Shazad, Usman; Yadav, Vinay Kumar

    In sample surveys, the problem of outliers is one of the most frequent and widest, whose solution is required to be obtained using statistical techniques. To overcome this problem, various robust regression methods are being developed such as LTS, LMS, LAD, Huber-M, Hample-M, Tukey-M and Huber-MM methods. This article presents the modified Hartley–Ross type ratio estimators to estimate the population mean in sample surveys. The proposed design is taken into account under the two suppositions one is that the study variable is a non-sensitive variable, which means that measurements on it do not embarrass participants in personal interviews and other is the sensitive variable, which means that measurement errors are introduced as a result of a few dishonest responses. The use of scrambling response models helps to reduce these measurement errors to some extent. The proposed estimator is found to be more efficient than the existing classical estimators. A Numerical illustration was performed on a real data set in R-Program Software to support of our findings.

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  • MakaleAçık Erişim2024
    Improved estimation of population variance in stratified successive sampling using calibrated weights under non-response

    Pandey, M. K.; Singh, G. N.; Zaman, Tolga; Al Mutairi, Aned; Mustafa, Manahil SidAhmed

    This paper introduces a new method to estimate the population variance of a study variable in stratified successive sampling over two occasions, while accounting for random non -response. The method uses a logarithmic type estimator and leverages information from a highly positively correlated auxiliary variable. The paper also presents calibrated weights for the new estimator and examines its properties through numerical and simulation studies. The results indicate that the suggested estimator is more effective than the standard estimator for estimating the population variance.

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  • MakaleAçık Erişim2019
    Is there prejudice against obese persons among health professionals? A sample of student nurses and registered nurses

    Yılmaz, Hacı Ömer; Yabancı Ayhan, Nurcan

    Purpose: The aim of this study was to assess whether there is prejudice toward obese persons among student nurses and registered nurses. Design and Method: A total of 379 participants were included (190 student nurses, 189 registered nurses). The Fat Phobia Scale and Belief About Obese Persons Scale were used to determine the prejudice. Findings: Registered nurses were found to have more negative prejudices toward obese persons than those of student nurses. These prejudices were determined to be influenced by the nurses' body mass index and having an obese person in the family. Practical Implications: Both groups had negative prejudices toward obese persons, there was a higher level of prejudice among the registered nurses. These prejudices can have a negative impact on healthcare practices.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
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